K Nearest Neighbors Project¶
In [85]:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.dpi'] = 150
Loading the DataSet:¶
In [87]:
df = pd.read_csv('KNN_Project_Data')
In [147]:
df.head(10)
Out[147]:
| XVPM | GWYH | TRAT | TLLZ | IGGA | HYKR | EDFS | GUUB | MGJM | JHZC | TARGET CLASS | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1636.670614 | 817.988525 | 2565.995189 | 358.347163 | 550.417491 | 1618.870897 | 2147.641254 | 330.727893 | 1494.878631 | 845.136088 | 0 |
| 1 | 1013.402760 | 577.587332 | 2644.141273 | 280.428203 | 1161.873391 | 2084.107872 | 853.404981 | 447.157619 | 1193.032521 | 861.081809 | 1 |
| 2 | 1300.035501 | 820.518697 | 2025.854469 | 525.562292 | 922.206261 | 2552.355407 | 818.676686 | 845.491492 | 1968.367513 | 1647.186291 | 1 |
| 3 | 1059.347542 | 1066.866418 | 612.000041 | 480.827789 | 419.467495 | 685.666983 | 852.867810 | 341.664784 | 1154.391368 | 1450.935357 | 0 |
| 4 | 1018.340526 | 1313.679056 | 950.622661 | 724.742174 | 843.065903 | 1370.554164 | 905.469453 | 658.118202 | 539.459350 | 1899.850792 | 0 |
| 5 | 1587.993461 | 667.420121 | 2072.048544 | 364.624958 | 905.011385 | 2229.178514 | 880.442566 | 568.698408 | 645.719217 | 2156.949990 | 1 |
| 6 | 1497.293624 | 822.435853 | 908.059366 | 671.797517 | 1280.270442 | 1910.066313 | 1052.812386 | 713.757499 | 703.951632 | 1088.493788 | 0 |
| 7 | 1172.820769 | 1166.958461 | 455.656588 | 445.572745 | 1109.894585 | 1045.551452 | 1301.821786 | 264.940137 | 945.023932 | 2307.922229 | 0 |
| 8 | 839.494647 | 1089.747059 | 653.699894 | 659.334590 | 1529.725972 | 1521.320489 | 1401.676227 | 433.025043 | 1289.063583 | 1690.149835 | 0 |
| 9 | 1028.689140 | 202.089774 | 1030.841860 | 429.109102 | 943.104815 | 1978.506855 | 653.178512 | 753.655291 | 1336.375960 | 1057.864848 | 1 |
In [89]:
#Dropping the 'TARGET CLASS' Feature:
db = df.drop('TARGET CLASS', axis = 1)
db.head()
Out[89]:
| XVPM | GWYH | TRAT | TLLZ | IGGA | HYKR | EDFS | GUUB | MGJM | JHZC | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1636.670614 | 817.988525 | 2565.995189 | 358.347163 | 550.417491 | 1618.870897 | 2147.641254 | 330.727893 | 1494.878631 | 845.136088 |
| 1 | 1013.402760 | 577.587332 | 2644.141273 | 280.428203 | 1161.873391 | 2084.107872 | 853.404981 | 447.157619 | 1193.032521 | 861.081809 |
| 2 | 1300.035501 | 820.518697 | 2025.854469 | 525.562292 | 922.206261 | 2552.355407 | 818.676686 | 845.491492 | 1968.367513 | 1647.186291 |
| 3 | 1059.347542 | 1066.866418 | 612.000041 | 480.827789 | 419.467495 | 685.666983 | 852.867810 | 341.664784 | 1154.391368 | 1450.935357 |
| 4 | 1018.340526 | 1313.679056 | 950.622661 | 724.742174 | 843.065903 | 1370.554164 | 905.469453 | 658.118202 | 539.459350 | 1899.850792 |
EDA¶
In [91]:
sns.pairplot(data = df, hue = 'TARGET CLASS', palette = 'mako' )
plt.show()